QSPedia: a curated parameter library to enable the rapid development of quantitative systems pharmacology models Project Summary/Abstract Quantitative Systems Pharmacology Modeling (QSPM) has been identified by the FDA and others as a key technology with the potential to bring down the cost of drug discovery and development. There have been some striking recent success where pharma, and the FDA have used QSPM to inform drug development, clinical decision making, and assess risk in regulatory decision making. However, despite some early success, adoption of QSPM has been limited to date. In the past this has been driven by two main factors, the computational complexity of simulating and analysing QSPM, and the scientific complexity of building the models themselves. There has been significant work by Applied BioMath and others in the QSP community on improving the computational performance of the modeling platforms. However, less attention has been paid to addressing the scientific complexity of building QSPM. QSPedia is meant to address this issue. QSPedia will be an integrated data environment of model parameter values and their associated scientific evidence that can be used by model developers to speed up the model development process. This centralized resource will employ automation and develop scalable methodologies to extract, validate and manage the biological data required for building QSP models which would be beyond the resources of an individual drug development group. We will leverage our existing modeling and simulation infrastructure for data validation and our inhouse modeling expertise to ensure the overall utility of QSPedia. We will assess the feasibility of developing a comprehensive parameter catalog by focusing in Phase-1 on a set of 36 drugs that can be described by a simple type of QSPM. We will explore the datasets and the information technologies we anticipate using to build QSPedia to develop a parameter catalog that described these 36 drugs and their targets. We will test the quality of the parameter set by having all parameter values reviewed by an expert. We will also create 36 QSPM using these parameter values to test that the models are accurate. If we are successful we anticipate that QSPedia will have a substantial impact increasing the use of QSPM in drug discovery. This will in turn have a positive impact on the cost of development and the quality of drugs brought to the market. Project Summary 1 of 1